Skip to Main content Skip to Navigation
New interface
Conference papers

Causal discovery for fuzzy rule learning

Lucie Kunitomo Jacquin 1 Aurore Lomet 1 Jean-Philippe Poli 1 
1 LS2D - Laboratoire Sciences des Données et de la Décision
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
Abstract : Fuzzy rules induction algorithms are usually based on statistical criteria such as correlation or on examples covering. Although these algorithms can be efficient to predict the outputs, some studies also aim to provide insight about the mechanism that links inputs and outputs. In our work, we focus on allying fuzzy logic, which is a suitable model for human-like information, and causality, which is a key concept for humans to generate knowledge from observations and also to build explanations. If a fuzzy premise causes a fuzzy consequence, then acting on the fuzzy premise will have an impact on the fuzzy consequence. This is not necessarily the case for common fuzzy rules based on correlation. Indeed, correlations may be due to some latent common cause of fuzzy premise and consequence, thus acting on the fuzzy premise will have no impact on the fuzzy consequence. This article proposes an approach to construct a set of causality-based fuzzy rules from crisp observational data. The idea is to identify causal relationships on the set of fuzzified inputs and outputs by well-known constraints-based causal discovery algorithms such as Peter-Clark and Fast Causal Inference. The causal discovery algorithms are combined with entropy-based conditional independent testing that avoids making hypotheses on the data distribution. Experiments are conducted to evaluate our approach in terms of ability to recover causal relationships between fuzzy sets in the presence of a latent common cause. The results illustrate the interest of our approach compared to a correlation-based approach and state-of-the-art approaches.
Complete list of metadata

https://hal-cea.archives-ouvertes.fr/cea-03805396
Contributor : Contributeur MAP CEA Connect in order to contact the contributor
Submitted on : Friday, October 7, 2022 - 11:30:32 AM
Last modification on : Tuesday, October 11, 2022 - 10:54:01 AM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2024-07-05

Please log in to resquest access to the document

Identifiers

Citation

Lucie Kunitomo Jacquin, Aurore Lomet, Jean-Philippe Poli. Causal discovery for fuzzy rule learning. IEEE International Conference on Fuzzy Systems, IEEE, Jul 2022, Padua, Italy. pp.1--8, ⟨10.1109/FUZZ-IEEE55066.2022.9882670⟩. ⟨cea-03805396⟩

Share

Metrics

Record views

20